Open Access
Issue
ITM Web Conf.
Volume 37, 2021
International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)
Article Number 01023
Number of page(s) 8
Section Innovative Technology for Sustainable Development
DOI https://doi.org/10.1051/itmconf/20213701023
Published online 17 March 2021
  1. B. Liu, W. Hsu and M. Yiming, “Integrating Classification and Association Rule Mining,” in 4th International Conference on Knowledge Discovery Data Mining (KDD), (1998). [Google Scholar]
  2. A. Veloso, W. MeiraJr. and M. J. Zaki, “Lazy Associative Classification,” in Sixth International Conference on Data Mining (ICDM’06), Hong Kong, China, (2006). [Google Scholar]
  3. Z. Huang, Z. Zhou and T. He, “Associative Classification With Knn,” Journal of Theoretical and Applied Information Technology, Vol. 49, no. 3, pp. 1013-1019, (2013). [Google Scholar]
  4. W. Hadi, Q. A. Al-Radaideh and S. Alhawari, “Integrating associative rule-based classification with Naïve Bayes for text classification,” Applied Soft Computing, Vol. 69, pp. 344-356, (2018). [Google Scholar]
  5. P. Tamrakar, S. S. Roy, B. Satapathy and I. S. P. Syed, “Integration of lazy learning associative classification with kNN algorithm,” in Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, (2019). [Google Scholar]
  6. S. A. Dudani, “The Distance-Weighted k-NearestNeighbor Rule,” IEEE Transactions on Systems, Man, and Cybernetics, Vols. SMC-6, no. 4, pp. 325-327, (1976). [Google Scholar]
  7. J. Gou, L. Du, Y. Zhang and T. Xiong, “A New Distance-weighted k-nearest Neighbor Classifier,” Journal of Information & Computational Science, Vol. 9, no. 6, pp. 1429-1436, (2012). [Google Scholar]
  8. L. Merschmann and A. Plastino, “A Lazy Data Mining Approach for Protein Classification,” IEEE Transactions On Nanobioscience, Vol. 6, no. 1, pp. 36-42, (2007). [Google Scholar]
  9. L. Merschmann and A. Plastino, “HiSP-GC: A Classification Method Based on Probabilistic Analysis of Patterns,” Journal of Information and Data Management, Vol. 1, no. 3, pp. 423-438, (2010). [Google Scholar]
  10. S. P. S. Ibrahim, C. K. R. and C. J. K. Kanthasamy, “LACI: Lazy Associative Classification using Information Gain,” IACSIT International Journal of Engineering and Technology, Vol. 4, no. 1, pp. 1-6, (2012). [Google Scholar]
  11. X. Zhang, G. Chen and Q. Wei, “Building a highly-compact and accurate associative classifier,” Applied Intelligence, Vol. 34, no. 1, pp. 74-86, (2011). [Google Scholar]
  12. P. Tamrakar and S. P. S. Ibrahim, “Attribute ranking based lazy learning associative classification,” ARPN Journal of Engineering and Applied Sciences, Vol. 13, no. 11, pp. 3698-3705, (2018). [Google Scholar]
  13. S. H. Ebenuwa, M. S. Sharif, M. Alazab and A. Al-Nemrat, “Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data,” IEEE Access, Vol. 7, pp. 24649-24666, (2019). [Google Scholar]
  14. J. Thomas, A. Joseph, I. Johnson and J. Thomas, “Machine Learning Approach For Diabetes Prediction,” International Journal of Information Systems and Computer Sciences, Vol. 8, no. 2, pp. 55-58, (2019). [Google Scholar]
  15. S. P. S. Ibrahim, K. R. Chandran and C. J. K. Kanthasamy, “Chisc-AC: compact highest subset confidence-based associative classification,” Data Science Journal, Vol. 13, pp. 127-137, (2014). [Google Scholar]
  16. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, Vol. 13, no. 1, pp. 21-27, (1967). [Google Scholar]
  17. E. Fix and J. L. HodgesJr., “Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties,” International Statistical Review, Vol. 57, no. 3, pp. 238-247, (1989). [Google Scholar]
  18. U. Lall and A. Sharma, “A nearest neighbor bootstrap for resampling hydrologic time series,” Water Resources Research, Vol. 32, no. 3, pp. 679-693, (1996). [Google Scholar]
  19. D. Dua and C. Graff, “UC Irvine Machine Learning Repository, ” (2019). [Google Scholar]

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